Oil spills detection: Challenges addressed in the scope of the SEAGULL project

Marques, Mario Monteiro; Lobo, Victor; Batista, Ricardo; Almeida, J.; Nunes, Maria de Fátima; Ribeiro, Ricardo; Bernardino, Alexandre

doi:10.1109/oceans.2016.7761019

Cited by 14 publications

(4 citation statements)

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“…The situation is further exacerbated by the impacts of climate change, which disrupts natural organisms and ecosystems in these regions [93]. In response to the challenges faced by Marine Protected Areas (MPAs) in conserving and safeguarding marine biodiversity and ecosystems, the use of drones has emerged as a promising solution to improving the required and relevant monitoring operations and management [32].…”

Section: Coastal Zone Ecological Monitoringmentioning

confidence: 99%

“…The three main types of UAVs used in marine rescue are maritime search and rescue UAVs, high-endurance UAVs, and ground-effect UAVs. Finally, drones have also been utilised in evaluating marine pollution and obtaining evidence of damage to the marine environment [16,[30][31][32]. Traditional satellite remote sensing monitoring has limitations regarding the high-frequency and rapid acquisition of sea area information.…”

Section: Introductionmentioning

confidence: 99%

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Applications, Evolutions, and Challenges of Drones in Maritime Transport

Wang,

Zhou,

Xing

et al. 2023

JMSE

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The widespread interest in using drones in maritime transport has rapidly grown alongside the development of unmanned ships and drones. To stimulate growth and address the associated technical challenges, this paper systematically reviews the relevant research progress, classification, applications, technical challenges, and possible solutions related to the use of drones in the maritime sector. The findings provide an overview of the state of the art of the applications of drones in the maritime industry over the past 20 years and identify the existing problems and bottlenecks in this field. A new classification scheme is established based on their flight characteristics to aid in distinguishing drones’ applications in maritime transport. Further, this paper discusses the specific use cases and technical aspects of drones in maritime rescue, safety, navigation, environment, communication, and other aspects, providing in-depth guidance on the future development of different mainstream applications. Lastly, the challenges facing drones in these applications are identified, and the corresponding solutions are proposed to address them. This research offers pivotal insights and pertinent knowledge beneficial to various entities such as maritime regulatory bodies, shipping firms, academic institutions, and enterprises engaged in drone production. This paper makes new contributions in terms of the comprehensive analysis and discussion of the application of drones in maritime transport and the provision of guidance and support for promoting their further development and integration with intelligent transport.

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Section: Coastal Zone Ecological Monitoringmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Applications, Evolutions, and Challenges of Drones in Maritime Transport

Wang,

Zhou,

Xing

et al. 2023

JMSE

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“…While grounded in an application to wildfire monitoring, the proposed coordinated planning and collaborative field coverage frameworks for teams of autonomous robots are broadly applicable to other domains and problems in which one is attempting to perform intelligent tracking and monitoring of many targets with few robotic sensors in large-scale operations [39]. Such applications include search-and-rescue [40,41,42], tracking of wildlife poachers [43,44,45], oil spill surveillance in oceans [46,47,48], border patrol and protection [49,39] and even air traffic control for urban air mobility [50,51,52]. Here, we discuss these potential applications.…”

Section: General Applicability Of the Proposed Frameworkmentioning

confidence: 99%

Multi-UAV Planning for Cooperative Wildfire Coverage and Tracking with Quality-of-Service Guarantees

Seraj¹,

Silva²,

Gombolay³

2022

Preprint

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In recent years, teams of robot and Unmanned Aerial Vehicles (UAVs) have been commissioned by researchers to enable accurate, online wildfire coverage and tracking. While the majority of prior work focuses on the coordination and control of such multirobot systems, to date, these UAV teams have not been given the ability to reason about a fire's track (i.e., location and propagation dynamics) to provide performance guarantee over a time horizon. Motivated by the problem of aerial wildfire monitoring, we propose a predictive framework which enables cooperation in multi-UAV teams towards collaborative field coverage and fire tracking with probabilistic performance guarantee. Our approach enables UAVs to infer the latent fire propagation dynamics for time-extended coordination in safety-critical conditions. We derive a set of novel, analytical temporal, and tracking-error bounds to enable the UAV-team to distribute their limited resources and cover the entire fire area according to the case-specific estimated states and provide a probabilistic performance guarantee. Our results are not limited to the aerial wildfire monitoring case-study and are generally applicable to problems, such as search-and-rescue, target tracking and border patrol. We evaluate our approach in simulation and provide demonstrations of the proposed framework on a physical multi-robot testbed to account for real robot dynamics and restrictions. Our quantitative evaluations validate the performance of our method accumulating 7.5× and 9.0× smaller tracking-error than state-of-the-art model-based and reinforcement learning benchmarks, respectively.

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“…Their contribution is specifically in application of camera fitted unmanned aerial vehicles in combating water pollution. The author in [5] documents various challenges involved in detection of spills, with emphasis on time lapse between actual spill and its detection.…”

Section: Related Workmentioning

confidence: 99%

Secure Data Transmission Framework for Internet of Things based on Oil Spill Detection Application

Abhijith¹,

Rameshbabu²

2021

IJACSA

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Internet of Things (IoT) is a leading technology which can interlink anything to Internet and makes everything to intelligent and smart. IoT is not just a single technology. IoT is a combination of various technologies like communication, data analytics, sensors and actuators, cloud computing, artificial intelligence, machine learning, etc. Applications of IoT are spread across various domains. IoT is most suitable for remote applications like underwater networks. One such application is oil spill detection in ocean. Oil spill in an ocean is a critical challenge that causes damages to marine ecosystem. Detection of oil spills in a real time manner helps to resolve the problem quickly to minimize the damage. IoT can be used to detect the oil spill by making use of sensors deployed at various locations of ocean. With a massive amount of sensors deployed and the huge amount of data associated with it, there remain concerns about the data management. Also amount of data generated in IoT based remote sensing network is usually enormous for the servers to process and many times data generated are redundant. Hence there is a need for designing a framework which addresses both aggregation of data and security related issues at various aggregation points. In this paper we are proposing a secure data transmission framework for detecting oil spill through IoT, which avoids redundant data transmission through data aggregation and ensures secure data transmission through authentication and light weight encryption.

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Oil spills detection: Challenges addressed in the scope of the SEAGULL project

Cited by 14 publications

References 1 publication

Applications, Evolutions, and Challenges of Drones in Maritime Transport

Applications, Evolutions, and Challenges of Drones in Maritime Transport

Multi-UAV Planning for Cooperative Wildfire Coverage and Tracking with Quality-of-Service Guarantees

Secure Data Transmission Framework for Internet of Things based on Oil Spill Detection Application

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